Google Inc.

Michael D Montalbo

Jason Noah Laska

Greg Duffy

Hiro Mitsuji

Gregory Rourk Nelson

Cameron Hill

Martin Davidsson

Tung Yuen Wan

The various implementations described herein include methods, devices, and systems for detecting motion and persons. In one aspect, a method is performed at a smart home system that includes a video camera, a server system, and a client device. The video camera captures video and audio, and wirelessly communicates, via the server system, the captured data to the client device. The server system: (1) receives and stores the captured data from the video camera; (2) determines whether an event has occurred, including detected motion; (3) in accordance with a determination that the event has occurred, identifies video and audio corresponding to the event; and (4) classifies the event. The client device receives information indicative of the identified events, displays a user interface for reviewing the video and audio stored by the remote server system, and displays the at least one classification for the event.

Methods and systems for detecting persons in a smart home environment

A computing system processes a video recording to identify a plurality of motion events, each corresponding to a respective video segment along a timeline of the video recording. The computing system identifies at least one object in motion within a scene depicted in the video recording and stores a respective event mask for each event. The computing system receives a definition of a zone of interest within the scene. In response to receiving the definition, the computing system determines, for each motion event, whether the respective event mask of the motion event overlaps with the zone of interest by at least a predetermined overlap factor, and identifies one or more events of interest from the plurality of motion events, wherein the respective event mask of each identified event of interest is determined to overlap with the zone of interest by at least the predetermined overlap factor.

Method and system for non-causal zone search in video monitoring

A computing system obtains a respective motion vector for each of a series of motion event candidates in real-time as said each motion event candidate is detected in a live video stream. In response to receiving the respective motion vector for each of the series of motion event candidates, the computing system determines a spatial relationship between the respective motion vector of said each motion event candidate to one or more existing clusters established based on a plurality of previously processed motion vectors, and in accordance with a determination that the respective motion vector of a first motion event candidate of the series of motion event candidates falls within a respective range of at least a first existing cluster of the one or more existing clusters, assigns the first motion event candidate to at least a first event category associated with the first existing cluster.

Method and system for cluster-based video monitoring and event categorization

A computing system receives a definition of a zone of interest within the scene depicted in the video steam. In response to receiving the definition of the zone of interest, the computing system determines, for each motion event detected in the video stream, whether a respective event mask of the motion event overlaps with the zone of interest by at least a predetermined overlap factor; and identifying the motion event as an event of interest associated with the zone of interest in accordance with a determination that the respective event mask of the motion event overlaps with the zone of interest by at least the predetermined overlap factor.

Growing community of inventors

Menlo Park, CA, United States of America

Michael D Montalbo